GoldStandardDB: Reproducibility Benchmark

• GoldStandardDB is a reproducibility benchmark comprising 50 articles used to compare system-generated judgments against expert annotations.
• It evaluates document-level reproducibility using metrics such as ACC and F1, with ARA reporting 61.68% accuracy and 50.07% F1 versus ReproScreener.
• The benchmark functions as an external validation target within ARA, employing a uniform workflow-graph reconstruction approach without benchmark-specific modifications.

Searching arXiv for the cited papers and related benchmark context. GoldStandardDB is a reproducibility benchmark dataset used for computational reproducibility assessment in "ARA: Agentic Reproducibility Assessment For Scalable Support Of Scientific Peer-Review" (Riehl et al., 4 May 2026). In that paper, it appears as one of three evaluation benchmarks for Agentic Reproducibility Assessment (ARA), alongside ReScience C and Repro-Bench, and is treated as a document-level reproducibility benchmark against which ARA’s assessments are compared with existing human annotations or benchmark labels. The paper does not provide a standalone construction narrative for GoldStandardDB in its main methods section; instead, it identifies the benchmark at a high level as originating from ReproScreener and Bhaskar et al. 2024, and reports its role, task setting, and headline evaluation results.

1. Benchmark identity and scope

Within the ARA evaluation framework, GoldStandardDB is presented as an external comparison set for reproducibility scoring rather than as a newly introduced dataset (Riehl et al., 4 May 2026). The benchmark table describes it as containing 50 articles and evaluates methods on ACC and F1 in the same general document-level reproducibility setting used for the other benchmarks.

The paper’s description is intentionally compact. It does not provide, in the main text, a dataset-specific narrative covering composition, source domains, annotation protocol, or exact label semantics beyond the benchmark-level reproducibility classification context. What is explicit is that GoldStandardDB functions as a benchmark for agreement between system-generated reproducibility judgments and pre-existing human or expert-derived assessments. This suggests that GoldStandardDB serves primarily as an evaluation target for reproducibility classification or scoring rather than as a dataset introduced for methodological development within the ARA paper itself.

Aspect	Reported description
Benchmark name	GoldStandardDB
Attributed source	ReproScreener / Bhaskar et al. 2024
Size	50 articles
Task setting	Document-level reproducibility benchmark
Reported metrics	ACC, F1
Role in ARA	External benchmark for reproducibility assessment

2. Position within the ARA evaluation suite

ARA is evaluated on three benchmarks: ReScience C, Repro-Bench, and GoldStandardDB (Riehl et al., 4 May 2026). GoldStandardDB occupies the role of a compact benchmark used to test whether the ARA pipeline generalizes beyond the datasets for which the paper provides more extensive narrative detail. The benchmark comparison is framed as a comparison to human annotations / expert reproducibility assessments.

A central feature of the evaluation context is that ARA operates strictly from the paper text. The paper emphasizes that ARA does not use external replication packages or web retrieval, whereas some comparators or human assessments may rely on richer information sources. For GoldStandardDB, this matters directly for interpretation: if benchmark labels reflect evidence not fully recoverable from the publication text alone, then the comparison is conservative for ARA. The paper states this conservatism explicitly at the benchmark level, and GoldStandardDB is included within that evaluation regime.

The ARA paper also does not present a GoldStandardDB-specific workflow reconstruction procedure. GoldStandardDB is treated uniformly with the other benchmarks at the level of extraction, scoring, and comparison. This uniformity is significant because it means the reported GoldStandardDB results are produced by the same generic document-analysis machinery rather than by benchmark-specific tuning.

3. Workflow-graph formalization used on GoldStandardDB

For GoldStandardDB, ARA applies its general workflow-graph reconstruction and scoring pipeline without benchmark-specific modification (Riehl et al., 4 May 2026). Given a paper $D$, ARA converts it into a directed workflow graph $G=(V,E)$, where nodes correspond to sources, methods, experiments, and sinks. The extraction is carried out by an LLM-based agent using structured prompts grounded in literal quotes from the paper.

At the micro level, each node receives an ordinal reconstructability score

$r(\cdot)\in\{1,2,3,4\},$

with the four levels defined as missing information, partial specification, mostly specified, and sufficient detail for independent reconstruction. These node-level scores are then aggregated stage-wise. For each stage $$k\in\{\text{sources},\text{methods},\text{experiments},\text{sinks}\}$$, the paper defines

$R_k=\frac{1}{|V_k|}\sum_{v\in V_k}r(v).$

The content score is a weighted aggregation across the four stages using fixed weights

$$(w_{\text{sources}}, w_{\text{methods}}, w_{\text{experiments}}, w_{\text{sinks}})=(0.30, 0.20, 0.20, 0.30).$$

In parallel, the structural score $R_s$ is computed from five graph-topology components: source connectivity, sink connectivity, identifier resolution, source-to-sink reachability, and largest weakly connected component, with weights

$(0.25,0.25,0.20,0.15,0.15).$

The final composite reproducibility score is

$R=\sqrt{R_c\cdot R_s}.$

For GoldStandardDB specifically, the paper’s main point is procedural consistency: the same extraction-and-scoring mechanism used on the other benchmarks is applied here, and the resulting reproducibility judgments are compared against the benchmark labels.

4. Reported performance and benchmark comparison

The ARA paper reports the following quantitative results for GoldStandardDB (Riehl et al., 4 May 2026). For ARA, ACC = 61.68% and F1 = 50.07%. The only explicit comparator reported in the benchmark table for GoldStandardDB is ReproScreener, with ACC = 43.56% and F1 = 36.66%.

The abstract highlights the GoldStandardDB comparison explicitly, stating that ARA achieves the highest accuracy reported on GoldStandardDB, 61.68% vs. 43.56%. This comparison is one of the paper’s principal benchmark-level claims. The benchmark table also reports parenthetical values for ARA on GoldStandardDB: ACC = 61.68 (32.94) and F1 = 50.07 (33.98). The table presents these as variability or spread indicators, and the broader consistency analysis in the paper states that repeated runs were used to estimate mean and standard deviation across configurations. The row itself does not restate the statistical interpretation.

Method	ACC	F1
ARA	61.68%	50.07%
ReproScreener	43.56%	36.66%

The paper does not report ReplicatorAgent on GoldStandardDB in the benchmark table. Consequently, the GoldStandardDB comparison in the published results is specifically an ARA-versus-ReproScreener comparison rather than a broader multi-baseline ranking.

5. Evaluation semantics and interpretive constraints

GoldStandardDB is used in the ARA paper as a document-level reproducibility benchmark, and the reported ACC and F1 are presented in the context of standard classification agreement against benchmark labels (Riehl et al., 4 May 2026). The paper does not give a GoldStandardDB-specific formal definition of metric computation, but the surrounding evaluation setup makes clear that the system output is compared with benchmark judgments derived from prior annotation or assessment work.

Several interpretive constraints follow directly from what the paper does and does not report. First, GoldStandardDB is only lightly characterized in the main narrative: beyond 50 articles, the paper does not provide source-domain breakdowns, annotation protocol details, or explicit label semantics. Second, no GoldStandardDB-specific confidence intervals or significance tests are reported in the main text. Third, the paper does not include a dedicated GoldStandardDB error analysis.

These omissions do not invalidate the benchmark’s use, but they do delimit what can be inferred from the ARA results alone. The reported numbers support comparison within the benchmark table, especially against ReproScreener, but they do not by themselves expose the internal heterogeneity of the dataset or the precise reasons for success and failure on individual articles. A plausible implication is that GoldStandardDB functions in the paper more as an external validation target than as an analytically unpacked corpus.

6. Limitations, caveats, and terminological scope

The principal GoldStandardDB-specific caveats in the ARA paper are indirect rather than dataset-specific (Riehl et al., 4 May 2026). ARA is document-only, so any benchmark comparison is conservative when benchmark labels may reflect evidence outside the publication text. The paper also provides no GoldStandardDB-specific error analysis, and its main narrative offers limited detail on how GoldStandardDB itself was constructed or scored in prior work.

A further issue is terminological scope. In the reproducibility-assessment setting, GoldStandardDB denotes the 50-article reproducibility benchmark used in ARA. A distinct usage appears in the chemical-benchmark literature, where GSCDB138 is presented as a gold-standard database for density-functional assessment rather than a reproducibility benchmark (Liang et al., 19 Aug 2025). The two should therefore not be conflated. In the ARA context, GoldStandardDB refers specifically to the benchmark attributed to ReproScreener and Bhaskar et al. 2024, and its significance lies in its role as an external dataset for document-level reproducibility evaluation.

Taken together, the published evidence supports a narrow but clear characterization: GoldStandardDB is a compact benchmark used to assess agreement between automated reproducibility judgments and prior human or expert assessments, and within that setting ARA reports 61.68% accuracy and 50.07% F1, outperforming the reported ReproScreener baseline on the same benchmark.